基于DSSW模型投资者情绪与股价指数关系研究

基于DSSW模型,本文构建了不同预期时间跨度下投资者情绪与股票价格的关系模型,并依据预期时间跨度不同,投资者情绪的均值和方差有不同的特征,分析得出长、短期投资者情绪对股票价格的不同影响。进一步以好淡指数作为投资者情绪的代理变量,检验不同预期时间跨度的好淡指数与股价指数的关系,验证理论分析结论。最后给出对策建议。     

h指数在期刊评价中的应用 ——以 《大学图书馆学报》 为例

提出了期刊动态与静态h指数的概念,并以<中国引文数据库>作为数据源,统计分析<大学图书馆学报>的动态与静态h指数,探讨其在时间序列上的分布特征及变动趋势,同时将结果与期刊其他评价指标进行对比分析.结果表明,用h指数测评期刊需要有个时间跨度;期刊h指数较之影响因子具有一定的优势,而且与期刊其他评价指标具有显著的相关性;期刊动态和静态h指数是从不同侧面对期刊的测评,其结果可以成为同行评议的重要参考信息.     

我国寿险需求影响因素的实证分析

应用自回归分布滞后模型对我国寿险需求进行了实证研究。与已有的研究相比，本文消除了保费收入数据中因统计口径变化带来的影响，建模时考虑了时间序列的平稳性，而且考查了更长的时间跨度。研究表明，国内生产总值的增长和寿险业自身的发展是寿险需求增长的根本原因，实际利率和少年儿童赡养(抚养)率对寿险需求也有显著的影响，社会的老龄化、预期通货膨胀率和不断提高的教育水平对寿险需求的作用并不显著。     

伴随催化分解的聚集集团动力学演化行为研究

文章研究了具有催化作用下的分解一聚集过程,从平均场理论出发,建立相应的Stooluchowski速率方程,通过理论计算进行求解,进而讨论各影响机制对聚集集团演化行为的作用,以及长时近似下各集团的动力学演化行为的规律。结果显示系统的动力学行为与分解反应指数v紧密相关,其中,两种分解产物的分布函数,在各种情况下始终满足传统标度率,而待分解的聚集集团只在v=0时,满足修正后的标度率。     

标度选择对群决策影响的研究

人们在研究群决策问题的时候很少关注标度问题,本文通过实例说明标度对群决策的影响是至关重要的。同时通过对标度问题的研究,给出了评价标度合理性的准则。对人们普遍使用的标度进行了简单的评价,说明了指数标度的合理性。     

网络数据库评价指标体系构建

针对网络数据库利用定性与定量相结合的方法,建立起一套客观、科学、合理的综合性评价指标体系.采用网上特尔菲法和基于指数标度的层次分析法.建立判断矩阵的过程中,采用"等差分级、等比赋值"的指数标度取代1-9标度系统,获得非常满意的一致性检验结果.     

DNA序列分析法在金融数据时间序列中的应用

通过线性分段将连续性的金融时间序列转化为离散性的字符序列,并基于DNA序列分析法,讨论了此类字符序列的标度特性,以及在金融数据时间序列预测中的可能应用     

一种计算H指数的简便方法

本文利用时间序列变换频率的方法给出了一种计算时间序列H指数的简便方法。该方法克服了利用R／S分析方法计算H指数的复杂性，文中给出了H指数与时间序列变换频率之间的关系式。     

基于竞争力择优的产业联盟知识转移网络动态演化模型

针对BA网络模型模拟现实世界的局限性,综合考虑产业联盟复杂网络的动态演化特性、竞争性等特点,构建了竞争力择优动态演化模型.该模型在考虑节点增加的同时,包含了节点的删除、旧节点之间竞争力择优的再生连接和反择优的删除连接.运用连续介质理论和平均场理论建立起与之对应的演化方程,导出该模型的度分布仍然具有无标度网络的一般性质,BA无标度网络是它的一种特殊情形.数值仿真的模拟结果验证了理论分析的正确性.研究结果表明,通过调节参数可以与现实中许多复杂网络的幂率指数相吻合,因此,改进的模型更具有适应性和真实性.     

基于标准差修正G1组合赋权的科技评价模型及实证

根据科学发展观内涵,从科技投入与产出、科技对经济和社会的影响等方面构建科学技术评价指标体系,通过标准差修正G1法确定指标权重,建立基于标准差修正G1组合赋权的科学技术评价模型,并对我国2001 ～2007的科学技术发展水平进行评价.通过标准差修正G1法确定组合权重,使得组合权重既反映了专家意见又反映了指标的数据信息,避免了在主观赋权中指标重要性标度人为主观确定缺少客观依据的问题.通过对中国的科学技术评价,找出了影响我国科技发展的主要影响因素,对国家制定科技发展的政策提供参考.     

Characterization of chaotic multiscale features on the time series of melt index in industrial propylene polymerization system

The chaos characteristics of melt index have been first explored, and the Hilbert–Huang transform method and time delay embedding method are applied to multiscale dynamic analysis on the time series of the melt index (MI) in the propylene polymerization industry. The research results show that the embedding delay is 2, the embedding dimension is 5, the correlation dimension D₂ is 1.57, and the maximum Lyapunov exponent is 0.143 for the melt index series, which provide clear evidence of chaotic multiscale features in the propylene polymerization process. Three intrinsic mode functions (IMFs) are decomposed from the melt index time series; the presence of non-integer fractal correlation dimension and positive finite maximum Lyapunov exponent are found in some IMF components. The PP melt index series are divided into two chaotic signals, a determined signal and a random signal respectively, and its complexity is therefore reduced. Furthermore, the coupling of subscale structures of the propylene polymerization is explored with the dimension of interaction dynamics and a robust algorithm for detecting interdependence. It is found that IMF(2) is the main driver in the coupling system of IMF(1)and IMF(2). All these provide a guideline for studying propylene polymerization process with chaotic multiscale theory and may offer more candidate tools to model and control propylene polymerization system in the future.     

Invention in the city: Increasing returns to patenting as a scaling function of metropolitan size

We investigate the relationship between patenting activity and the population size of metropolitan areas in the United States over the last two decades (1980-2001). We find a clear superlinear effect, whereby new patents are granted disproportionately in larger urban centers, thus showing increasing returns in inventing activity with respect to population size. We characterize this relation quantitatively as a power law with an exponent larger than unity. This phenomenon is commensurate with the presence of larger numbers of inventors in larger metropolitan areas, which we find follows a quantitatively similar superlinear relationship to population, while the productivity of individual inventors stays essentially constant across metropolitan areas. We also find that structural measures of the patent co-authorship network although weakly correlated to increasing rates of patenting, are not enough to explain them. Finally, we show that R&D establishments and employment in other creative professions also follow superlinear scaling relations to metropolitan population size, albeit possibly with different exponents.     

Optimal fractal-scaling analysis of human EEG dynamic for depth of anesthesia quantification

The depth of anesthesia estimation has been of great interest in recent decades. In this paper, we present a new methodology to quantify the levels of consciousness. Our algorithm takes advantage of the fractal and self-similarity properties of the electroencephalogram (EEG) signal. We have studied the effect of anesthetic agents on the rate of the signal fluctuations. By translating these fluctuations with detrended fluctuation analysis (DFA) algorithm to fractal exponent, we could describe the dynamics of brain during anesthesia. We found the optimum fractal-scaling exponent by selecting the best domain of box sizes, which have meaningful changes with different depth of anesthesia.Experimental results confirm that the optimal fractal-scaling exponent on the raw EEG data can clearly discriminate between awake to moderate and deep anesthesia levels and have robust relation with the well-known depth of anesthesia index (BIS). When the patient's cerebral states change from awake to moderate and deep anesthesia, the fractal-scaling exponent increases from 0.8 to 2 approximately. Moreover, our new algorithm significantly reduces computational complexity and produces faster reaction to transients in patients’ consciousness levels compared to other algorithms and technologies.     

Document replication strategies for geographically distributed web search engines

Large-scale web search engines are composed of multiple data centers that are geographically distant to each other. Typically, a user query is processed in a data center that is geographically close to the origin of the query, over a replica of the entire web index. Compared to a centralized, single-center search engine, this architecture offers lower query response times as the network latencies between the users and data centers are reduced. However, it does not scale well with increasing index sizes and query traffic volumes because queries are evaluated on the entire web index, which has to be replicated and maintained in all data centers. As a remedy to this scalability problem, we propose a document replication framework in which documents are selectively replicated on data centers based on regional user interests. Within this framework, we propose three different document replication strategies, each optimizing a different objective: reducing the potential search quality loss, the average query response time, or the total query workload of the search system. For all three strategies, we consider two alternative types of capacity constraints on index sizes of data centers. Moreover, we investigate the performance impact of query forwarding and result caching. We evaluate our strategies via detailed simulations, using a large query log and a document collection obtained from the Yahoo! web search engine.     

Value at Risk模型及其在香港股市中的实证分析

在本文中,我们就已有的各种Value at Risk(VaR)模型进行了分析,并就仅含单个股指的投资组合进行了具体的计算.为了对每个模型的有效性进行评价,我们采用Kupiec的Back-test检验进行比较.主要的结论是进一步验证了股指收益率的分布不是正态的,而是具有厚尾性;条件VaR模型优于非条件VaR模型;对股指收益率进行VaR估计时,最重要的是变易率聚类性.     

中国创业投资退出回报及其影响因素研究

本文采用1997-2005年我国66个创业投资退出项目的数据,研究了创业投资退出回报率及其影响因素。研究结果表明:我国创业投资平均退出回报率为158%,每1元的投资平均经过1.97年退出;退出回报与投资期限显著负相关,与注资阶段、投资规模显著正相关;退出回报与行业类型、创投类型、退出方式以及IPO地点没有显著的相关关系;投资年份越晚,退出回报越高;上海和深圳两地的项目退出回报水平相对较高。本文进而提出了相关的政策建议。     

Indicators for complex innovation systems

Performance indicators such as national wealth (GDP per capita), R&D intensity (GERD/GDP) and scientific impact (citations/paper) are used to compare innovation systems. These indicators are derived from the ratio of primary measures such as population, GDP, GERD and papers. Frequently they are used to rank members of an innovation system and to inform decision makers. This is illustrated by the European Research Area S&T indicators scoreboard used to compare the performance of member states.A formal study of complex systems has evolved over the past few decades from common observations made by researchers from many fields. Complex systems are dynamic and many of their properties emerge from the interactions among the entities in them. They also have a propensity to exhibit power law or scaling correlations between primary measures used to characterize them.Katz [Katz, J.S., 2000. Scale independent indicators and research assessment. Science and Public Policy 27, 23-36] showed that scientific impact (citations/paper) scales with the size of the group (papers). In this paper it will be shown that two other common measures, R&D intensity and national wealth, scale with the sizes of European countries and Canadian provinces. Some of these scaling correlations are predictable. These findings illustrate that a performance indicator derived from the ratio of two measures may not be properly normalized for size.This paper argues that innovation systems are complex systems. Hence scaling correlations are expected to exist between the primary measures used to characterize them. These scaling correlations can be used to construct scale-independent (scale-adjusted) indicators and models that are truly normalized for size. Scale-independent indicators can more accurately inform decision makers how groups of different sizes contribute to an innovation system. The ranks of member groups of an innovation system by scale-independent indicators can be subtly and profoundly different than the ranks given by conventional indicators. The differences can result in a shift in perspective about the performance of members of an innovation system that has public policy implications.     

上海证券市场的分形结构

本文应用赫斯特提出的Ｒ／Ｓ分析方法研究了上海证券市场股票收益的波动规律。结果显示上海的股票收益遵循有偏的随机游走过程或分析布朗运动,人格不能对信息做出及时充分地反映,收益序列呈现出持久性,今天的股票价格影响未来,时间是重要的,就像一块卵石落入水中,今天的事件在时间中像涟漪那样向前扩散,涟漪逐渐减弱直至在实际上消失。     

Unfolding polyelectrolytes in trivalent salt solutions using dc electric fields: A study by Langevin dynamics simulations

We study the behavior of single linear polyelectrolytes condensed by trivalent salt under the action of electric fields through computer simulations. The chain is unfolded when the strength of the electric field is stronger than a critical value. This critical electric field follows a scaling law against chain length, and the exponent of the scaling law is −0.77(1), smaller than the theoretical prediction, −3ν∕2 [R. R. Netz, Phys. Rev. Lett. 90, 128104 (2003)], and the one obtained by simulations in tetravalent salt solutions, −0.453(3) [P.-Y. Hsiao and K.-M. Wu, J. Phys. Chem. B 112, 13177 (2008)]. It demonstrates that the scaling exponent depends sensitively on the salt valence. Hence, it is easier to unfold chains condensed by multivalent salt of a smaller valence. Moreover, the absolute value of chain electrophoretic mobility increases drastically when the chain is unfolded in an electric field. The fact that the mobility depends on electric field and on chain length provides a plausible way to impart chain-length dependence in free-solution electrophoresis via chain unfolding transition induced by electric fields. Finally, we show that, in addition to an elongated structure, a condensed chain can be unfolded into a U-shaped structure. The formation of this structure in our study is purely a result of the electric polarization, not of the elastohydrodynamics dominated in sedimentation of polymers.     

Finite-size scaling of O(n) systems at the upper critical dimensionality

Logarithmic finite-size scaling of the O(n) universality class at the upper critical dimensionality (d_c = 4) has a fundamental role in statistical and condensed-matter physics and important applications in various experimental systems. Here, we address this long-standing problem in the context of the n-vector model (n = 1, 2, 3) on periodic four-dimensional hypercubic lattices. We establish an explicit scaling form for the free-energy density, which simultaneously consists of a scaling term for the Gaussian fixed point and another term with multiplicative logarithmic corrections. In particular, we conjecture that the critical two-point correlation g(r, L), with L the linear size, exhibits a two-length behavior: follows governed by the Gaussian fixed point at shorter distances and enters a plateau at larger distances whose height decays as with a logarithmic correction exponent. Using extensive Monte Carlo simulations, we provide complementary evidence for the predictions through the finite-size scaling of observables, including the two-point correlation, the magnetic fluctuations at zero and nonzero Fourier modes and the Binder cumulant. Our work sheds light on the formulation of logarithmic finite-size scaling and has practical applications in experimental systems.